This article unpacks a 2026 paper arguing that under finite resources, narrowly focused AI systems consistently outperform general-purpose ones. It draws on the no-free-lunch theorem, examples from biology and markets, and machine learning phenomena like negative transfer, mixture-of-experts, and AlphaFold’s task-specific success.

• The no-free-lunch theorem means gains on one task distribution necessarily cost performance on others, so under finite compute/data/time, specialized models win.
• Even massive "general" models rely on mixture-of-experts routing, quietly embedding narrow specialists inside them rather than being truly general.
• Negative transfer in multi-task training shows shared capacity actively creates conflict, dragging down individual task accuracy.
• Biology and markets both confirm the pattern: evolution favors niche specialists over generalists, and focused firms outcompete unfocused ones.